Foundations of Data Science Bootcamp
Practical introduction to Python, SQL, data wrangling, exploratory analysis, and basic machine learning concepts.
Instructor: Vijay Gohil
Term: Fall
Location: New York University
Time: Fall 2022
Course Overview
This course introduced students to the practical foundations of modern data science. By the end of the bootcamp, students were able to:
- Work comfortably with Python and SQL for data tasks
- Clean, explore, and visualize real-world datasets
- Train and compare baseline machine learning models
- Present findings from data with clear analysis and supporting material
Prerequisites
- Comfort with introductory programming
- Basic probability and statistics
- Willingness to work with hands-on assignments
Textbooks
- “Python Data Science Handbook” by Jake VanderPlas
- “Hands-On Machine Learning” by Aurélien Géron
Grading
- Weekly exercises: 40%
- Final project: 50%
- Participation: 10%
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Sept 19 | Introduction to Python and SQL Overview of Python and SQL basics | |
| 2 | Oct 11 | Data Wrangling Libraries Intro to Numpy, pandas and scikit-learn. | |
| 3 | Oct 22 | Exploratory Data Analysis Introduction to exploratory data analysis techniques. | |
| 4 | Oct 29 | Data Visualization and Cleaning Data visualization techniques and data cleaning methods. | |
| 5 | Nov 4 | Data Science Project Students work on a comprehensive data science project. |